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sora_generate_video

Generate a video from a text prompt. Customize model, resolution, duration, and orientation to match your vision.

Instructions

Generate an AI video from a text prompt using Sora.

This is the primary way to create videos - describe what you want and Sora
will generate a video matching your description.

Use this when:
- You want to generate a video from a text description
- You don't have reference images
- You want creative AI-generated video content

For image-to-video generation, use sora_generate_video_from_image instead.
For character-based video generation, use sora_generate_video_with_character.

Returns:
    Task ID and generated video information including URLs and state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video to generate. Be descriptive about the scene, action, style, and mood. Examples: 'A cat running on the river', 'A futuristic cityscape with flying cars at sunset', 'A person walking through a snowy forest'
modelNoSora model version. 'sora-2' is the standard model. 'sora-2-pro' offers higher quality and supports 25-second videos.sora-2
sizeNoVideo resolution. 'small' for lower resolution, 'large' for higher resolution.large
durationNoVideo duration in seconds. Options: 10, 15, or 25 (25 only available with sora-2-pro model).
orientationNoVideo orientation. 'landscape' for horizontal (16:9), 'portrait' for vertical (9:16).landscape

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It states the tool creates a video, which implies a non-read action, and mentions it returns task ID and video info. It could note that generation may take time, but the transparency is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections and front-loaded with the main action. It is slightly long but each section adds value. No unnecessary sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, many siblings, output schema exists), the description covers purpose, usage guidelines, and parameter context thoroughly. The output schema handles return value details, so no gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description does not add parameter-level detail beyond the schema, but the schema already provides sufficient descriptions for all 5 parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates an AI video from a text prompt using Sora, a specific verb+resource. It distinguishes from siblings like sora_generate_video_from_image and sora_generate_video_with_character by specifying when to use each.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly lists when to use this tool (e.g., 'when you want to generate a video from a text description') and when not to, with direct references to alternative tools for image-to-video and character-based generation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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